151

Incorporating Wind Damage in Potential Flood Loss Estimation
Srikantha HERATH* and Yi WANG
Institute for Sustainability and Peace, United Nations University
5-53-70, Jingumae, Shibuya-ku, Tokyo 150-8925, Japan*e-mail: herath@unu.edu

Abstract
Despite concentrated global efforts, flood losses worldwide are increasing. This trend is expected to
escalate further due to climate change. The fourth IPCC report has explicitly warned against increase offlood disasters and recent studies show increased rainfall intensities and intensification of typhoons willcontribute to further increases in flood losses. Estimation of potential flood losses can play an important rolein investing for prevention as well as for developing financial instruments to reduce flood risks. However
only a few flood loss estimation models consider damages caused by wind effects during flooding. The
main objective of this paper is to review and discuss the possibility of incorporating wind induced damage
in urban flood loss estimation. First, an overview of the flooding loss and damage models is presented. Then,the applicability of flood loss estimation with inundation using loss function and inundation modeling is
introduced as an example in the Ichinomiya River basin, Japan. Finally, an approach to estimate wind damage loss on urban buildings during flooding events is introduced. The flood damage case study is thenanalyzed to assess wind related damage.
Key words: loss estimation, urban flood, wind damage

1. Introduction
Disaster Risk Reduction, Global Review, 2007, prepared by United Nations (UN) analyzed the global
trends of disasters and main issues needed to be addressed in reducing disaster risks. The analysis is mainly
based on the data available in EM-DAT Emergency
Events Database (OFDA/CRED, 1988). Recent disaster
trends from 1980 to 2005 reveal that while mortality
associated with geologic hazards has increased since
1990's mainly due to 2003 Bam earthquake, 2004 Indian
Ocean earthquake and 2005 Kashmir earthquake,mortality associated with climate data has remained
unchanged while that associated with droughts havedramatically reduced. This trend in stabilizing mortality
is significant when the same data sets show that numberof disasters has almost doubled between 1995 and 2005.
This may be due to an increased effectiveness of warning and preparedness as well as a rapid increase ofreports of small scale climatic hazards that does notcause deaths. The same analysis also shows that if megadisasters with over 10,000 deaths are excluded mortality
associated with climatic disasters is increasing at a rate
faster than global population increase (Burby, 1998).
In the case of economic risks, there is a clear upward
trend of economic losses according to data compiled by
Munich Re. (Munich Re., 1997). From the data of great
natural disasters from 1950-2006, climatic events (windGlobal Environmental Research
13/2009: 151-159
printed in Japan

storms, floods and extreme temperatures) comprise 71%
of all large scale economic disasters and accounts for
69% of total economic losses while causing 49% ofmortalities. Thus, the economic impact of climatic
events has a much higher share among all disasters compared to mortality. In this distribution, floods comprise
25% of all events, accounting for 24% of total economic
losses, while causing 7% of fatalities (Munich Re.,2001).
Disasters result from a combination of natural hazards events and the degree of exposure and vulnerabilityof the society. The trend in increasing economic risk ofextreme events in recent times can be attributed to theincreased concentration of population and assets invulnerable areas. According to Munich-Re there had
been 20 ‘great natural catastrophes’ between 1950 and1959 that has caused US$ 38 billion economic losses
where as the number of such disasters has increased to
82 between 1990 and 1999 causing US$ 535 billion
damage. It is clear that damage from ‘catastrophic
events’, rare events that go beyond the coping capacityof infrastructure, inflict heavy losses and these losses
are increasing.
In addition to the increase of exposure, climatechange increases risk from natural hazards. The recently
released Intergovernmental Panel on Climate Change(IPCC) report 4 has explicitly warned against increaseof flood disasters as a consequence of climate change.
©2009 AIRIES152

S. HERATH and Y. WANG

The working group II that focuses on Climate Change
Impacts, Adaptation and Vulnerability, has identified
following risks; In Asia, glacier melt in the Himalayas is
projected to increase flooding, and rock avalanches fromdestabilized slopes. Coastal areas, especially heavily
populated mega-delta regions in South, East and Southeast Asia, will be at greatest risk due to increased flooding from the sea and, in some mega-deltas, flooding
from the rivers. Europe will experience increased risk ofinland flash floods, and more frequent coastal flooding
and increased erosion (due to storms and sea-level rise).
In Australia and New Zealand, on going coastal
development and population growth in areas such as
Cairns and Southeast Queensland (Australia) and Northland to Bay of Plenty (New Zealand), are projected toexacerbate risks from sea-level rise and increases in theseverity and frequency of storms and coastal flooding by
2050.
The current characteristics of flood risk increase,coupled with intensification of flood risks associatedwith climate change, require increased efforts to reduce
future flood losses. The current flood risk reduction
emphasis on saving lives has produced positive results
as indicated by the trends discussed above. However,this approach should be complemented by protecting
livelihoods and economic assets. The concentration of
economic assets in areas exposed to climate hazards
would continue to grow and will not be protected by
improved early warning, preparedness and response
although they are effective against reducing mortality.
This protection should be provided by safe buildings,
developing basic flood protection infrastructure, risk
sensitive planning and ensuring adequate investment.
Use of financial instruments in reducing basin wide
flood risk, need to be utilized more to make flood reduction strategies sustainable. In this regard assessment ofpotential economic losses is a pre-requisite for allocate
necessary investment as well as to establish safetymechanisms to finance rapid recovery and absorb losses.

2. Estimating Potential Flood Losses
There are basically two methods in carrying out
flood damage estimations. One is to carry out a thorough questionnaire survey of affected population andproperties to estimate the incurred loss after a flood
disaster. The other is to use what are known as stagedamage functions which describe the damage extent todifferent types of property for a given inundation depth
and inundation duration. The second approach can also
be used to estimate potential flood loss for an anticipated flood if the resulting inundation can be projectedwith reasonable accuracy. Some of the most important
issues in using this method for flood loss estimation are
obtaining detailed flood parameters such as flow velocity, depth and duration at any given location; properclassification of damage categories considering natureof damage; and establishment of functions between
flood parameters and damage for different damage

categories (Dutta et al., 2003). Generally, flood damage
functions are determined using a specified relationshipbetween flood characteristics (usually depth) and theextent of economic damage (Jonkmana et al., 2008).
Stage damage curves were first proposed in the USA in
the 1960s (White, 1964; Kates, 1965). Since then,methods for flood damage estimation have been developed in several other countries (Penning-Rowsell andChatterton, 1977, Parker et al., 1987, Dutta et al. 2003).
Most stage damage functions include water depth as themain determinant of direct damage. Kreibich et al.
(2005) and Thieken et al. (2005) also investigated theinfluence of other factors, such as flood duration, contamination and preparedness for flood damage based ondata for the 2002 floods in Germany. However, different
countries or agencies define the flood damage in different ways. For example, the U.S.A National Weather
Service (NWS) defines flood damage more narrowly
than many other agencies. Emergency management
agencies generally include both river and coastal flooding whenever water rises to overflow land that is notnormally submerged. In contrast, the NWS estimates
include only flooding whose primary cause is rainfall,snowmelt, or river flows, excluding flooding caused bywind-driven waves associated with coastal storms or
hurricanes. In recent decades, there is an increasing recognition of the importance of considering combined
impacts of floods associated with strong winds, but noscientific analysis of the problem has been made so far.
One of the reasons is that it is sometimes impossible toseparate damage by flood and other storm-related causes(e.g. wind, hail, snow, or ice). Typically, the total losses
are labeled as flood damage if heavy rain or river flows
are considered to be the primary cause. Thus, the flood
damage estimates are sometimes inflated by effect ofother causes. Conversely, flood damage may be omitted
when the major cause of damage is wind (hurricanes,
tornadoes), snow, or ice.
The main objective of this paper is to review and discuss the possibility of incorporating wind induced damage in urban flood loss estimation.

3. Methodology for Flood Loss Assessment
Given the complex interrelated processes that cancause and influence floods, defining and classifying allof them is not simple. For example, flood is defined as“the presence of water where water does not normallyappear” (OED, 2003), or “a temporary covering of land
by water as a result of surface waters (still or flowing)
escaping from their normal confines or as a result ofheavy precipitation” (Munich Re, 1997), or “significant
rise of water level in a stream, lake, reservoir or acoastal region.” (UNDHA, 1992). Flood type definitions
often reflect both the source of the event (coast, river)and the flood characteristics (water depth, rise rate).
Three types of floods are distinguished as: coastal, riverand flash floods (Berz et al., 2001; French and Holt,
1989). Tsunamis and tidal waves are generally treated asIncorporating Wind Damage in Potential Flood Loss Estimation

separate hazards, although they also result in flooding.
Also dam breaks are often considered as distinct hazards,
as they are considered “manmade” events and floods as“natural” disasters.
Accordingly, the consequences of a flood encompass
multiple types of damage, i.e. they are multi-dimensional. In Table 1, a classification of various types ofdamages characterizing flood and wind events was
summarized (Smith, 1994; Dutta et al., 2003; Kelman
and Spence, 2004; etc.). There is a distinction between
direct damages inside the flooded area and indirect damages that occur outside the flooded area (Jonkmana et al.,2008). Direct economic losses are caused mainly bymaterial losses and in general it is possible to classify
them into the loss of property, losses to the infrastructure and also to the environment. Another distinction ismade between tangible damages that can be priced, andintangible damages for which no market prices exist.
Direct costs are closely connected to a flood event andthe resulting physical damage. In addition to immediate
losses and repair costs they include short-term costs
stemming directly from the flood event, such as floodfighting, temporary housing, and administrative assistance. By contrast, indirect costs are incurred in an extended time period following a flood. They include lossof business and personal income (including permanent
loss of employment), reduction in property values,
increased insurance costs, loss of tax revenue, psychological trauma, and disturbance to ecosystems. They
tend to be more difficult to account for than direct costs(Pielke et al., 2002).
The spectrum of damages that a flood brings about
includes economic, political, social, psychological, ecological and environmental damages, all of which areoften intertangled in a complex network of modern
societies. Each of them alone cannot represent an intricate disaster phenomenon, rather all of them together

Table 1

contribute to the compound picture of disaster consequences. In fact, each of the mentioned damage dimensions needs a model of its own (Jonkmana et al., 2008).
The methods for the estimation of direct economic damage to physical objects (such as structures, houses) are
well established, i.e., the conventional way of flood
damage estimation in different countries around theworld is the stage-damage functions defining therelationship between flood parameters and possible
damage, which are derived based on historical flood
damage information, questionnaire survey, laboratory
experiences, etc. (Krzysztofowicz and Davis, 1983; Kok
et al., 2005). However, the methods for the estimation ofindirect and intangible damage are less well developed
so far (Stuyt et al., 2003, Ahern et al., 2005). In addition,
the height of losses is also influenced by the flooding
hazard quantified by flood characteristics such as water
depth and flow velocity, the duration of flooding andrate of water level increase, the temperature of the waterand its quality, sediment transport and some other factors (Kelman and Spence, 2004). Climatic factors suchas wind velocity, air temperature and humidity, ice
development, etc. are also of importance. Especially,wind and waves often suddenly change water velocity
and depth. During a storm surge flood, a sudden shift inwind direction may permit the sea to swiftly drain away
in a phenomenon known as an ebb surge (Heneka andRuck, 2004). The physical forces and pressures createdby the sudden retreat of water can exceed those imparted
by the ingress of water into a community. As well,
powerful gusts of wind, spikes far above the 3-s mean,
may destabilize a building under pressure from theflood’s lateral pressures. In risk analysis, only a few of
the above-mentioned factors are usually taken into
account, namely water depth and velocity, and the typeand foundation of the structure (Kreibich et al., 2005;
NAP, 1999).

Different dimensions of flood and wind damages.

Tangible and priced

Intangible and unpriced

InjuryResidential building
Fatalities
Industrial area
Inconvenience and moral damage
Recreational and sports facilities,
AnimalShopping area, Gas station
Environmental loss
Water work, water and gas supply
Cultural loss
Sewerage, road
Roads, utility & communication infrastructure Utilities and communication
Inconvenience and moral damage
Capital asset & inventory
Agricultural land & cattle
VehicleBusiness interruption (inside the flooded area)
Clean up cost, etc
Menace of infections and epidemics
Indirect Temporary line, bypasses, housing
Standby transport facilities
Psychological traumas
Consumption outside the flooded area
Undermined trust in public authorities
Additional travel expense and time delay
Societal disruption
Hygiene problem
Direct153

154

S. HERATH and Y. WANG

4. Assessing the Applicability of Flood Loss
Estimation with Inundation Forecast
A number of studies are reported in literature that
describes stage-damage functions derived from post
flood damage analysis (Parker et. al., 1987; Smith,
1994; etc.). In Japan and United Kingdom, the procedures are standardized to estimate flood damage for any
part of the country using normalized stage-damage functions. The main purpose of these procedures is appraisalof flood control projects through standardized economic
loss assessment. The approach can be used to estimate
the effectiveness of a particular flood control project interms of benefit compared with no-flood control mechanism scenario.
The methodology of flood loss estimation is outlined
through the Figs. 1, 2 and 3. Figures 1 and 2 show
samples of depth damage functions for Japan established through field sampling of flood events carried out
continuously since 1950’s for content and structural
damage of residential buildings. In order to estimate thepotential flood loss, firstly, the flood inundation map isprepared as shown in the top layer of Fig. 3, either fromnumerical simulation corresponding to a future scenario
or from past inundation data, in a GIS environment withhigh spatial resolution. Next, an asset map categorizedaccording to available depth-damage functions is prepared and the properties distributed at same grid resolution as in the inundation map. Using the depth-damage
functions and these two layers, the distribution of flood
loss can be estimated as shown in the last layer of the
map in Fig. 3.
The above methodology was applied in a case study
to assess the effectiveness of different river improvement works in reducing flood losses. The study area is amoderate size basin, named Ichinomiya river basin, withan area of 220 km2, located in the Chiba prefecture,
Japan between latitude 35°18´N to 35°30´N and longitude 140°10´E to 140°25´E. The mean annual rainfall isapproximately 1,700 mm and total population within thebasin is about 144,000 mainly concentrated in the urban
areas in lower flat part of the basin. The basin has suffered a large scale flood in 1996 and a detailed fieldsurvey has been carried out to assess the total flood
damage. The main categories of depth-damage functions
used in Japan are shown in Table 2. In this study thefollowing procedures were carried out.
Established a detailed GIS using remote sensing andadministrative data at 50 m grid scale.
Estimated the flood loss using actual flood heights
observed in the ground and compared with the flood
damage assessment carried out using the GIS.
Carried out numerical simulation to establish inundation map to estimate potential flood loss and assessed
the accuracy of predictions compared to the field survey
as well as estimations done with actual flood heightobservations.
Details of these studies are given in Herath et. al.
(1999) and Dutta et. al. (2003). The main results of thestudies were:
1. The flood loss estimation from the established GIS
using the measured water heights match very wellwith the flood loss estimated from field survey. Thismeans that asset distribution is adequately represented in the GIS.
2. Damage estimates from actual water levels and thosefrom the simulated water levels tend to have somediscrepancy in the structural damage assessment,
where as the content damage is estimated correctly.

Fig. 1 Depth damage function for residential content.

Fig. 2 Depth damage function for residential structure.

Fig. 3 Procedure for flood loss estimation.

Incorporating Wind Damage in Potential Flood Loss Estimation

155

Table 2 Main categories of depth-damage functions used in Japan.

DamageCategory
Urban
Residential
DamageUrban
Industrial
DamageDetails considered

Crop Damage

Nine types of major crop classes

Normalizing
parameters
Floor area, region

Structure

WoodenContents

Non-wooden All types

Household unit

Structure

Ten types of industry classes

Content

Ten types of industry classes

Number of
employees, region

Flood depth

Crop area, production Flood depth,
per crop, unit price per duration, season
crop
6000

Surveyed Damage

Simulated Damage

5000

Damage (1000 Yen)

This error could be due to discrepancies associatedwith inundation modeling as well as use of grid averaged elevation for deriving damage coefficients.
The comparisons of different estimates are shown in
Fig. 4.
3. The methodology adopted can provide information
on flood loss distribution, enabling to understand investment that can produce the maximum benefits.
The Fig. 5 shows the spatial distribution of flood
damages to residential content.

Influencing flood
parameters
Flood depth

4000
3000
2000
1000

5. Estimating wind Damage on Urban
Buildings
Despite the enormous impacts of floods on global
scale a limited number of models are available for theestimation of loss of damage caused by floods. All of
the models include some kinds of damage function
which relates damage to flood related characteristics.
Few of the models consider damage to urban buildingsin the flooded area caused by wind effects during flooding. Table 3 shows the general descriptions of urban
building’s damage due to wind (Smith, 1994; Kelman
and Spence, 2004; Heneka and Ruck, 2008; etc). Basically, it can be categorized as direct and indirect damage.
The direct building damage occurs most frequently toroofs, walls, claddings and openings. Indirect building
damage due to wind borne debris is dominated bybroken trees. Wind damage estimation can be done
either at large spatial units or at individual building unit
scales. The methodology described above for flood
damage estimation can be carried out for individual
building units and it can be combined with wind damage
estimated either at mesoscale or at individual building
scale.
Mesoscale assessments of storm damage aim at loss
determination on the spatial unit of postal-code zones ormunicipalities with a country-wide extent (Kelman andSpence, 2004; Heneka and Ruck, 2004). For mesoscale
damage assessment, some models have been developed
which can be summarized as qualitative and quantitativemodels. The latter are subdivided into empirical, theoretical and stochastic models (Heneka and Ruck, 2008).
Qualitative models describe the consequences of extreme wind speeds by means of their visual effects ofnatural phenomena on structures. They calculate building damage in relation to available meteorological and0
Residential
Structure

Residential
Content

Non Res.
Structure

Non Res.
Content

Non Res.
Stock

Fig. 4 Comparison of flood loss estimations from different
damage coefficients and inundation estimates (Unitof Y-axis: 1000 Yen).

Fig. 5 Distribution of residential flood content damage.

structural information such as wind speeds, storm duration, and building type.
5.1 Building damage due to wind
For the development of wind damage functions,where the storm damage can be assigned to a certain
wind speed, the investigation of the vulnerability ofstructures during winds is necessary. An overall schemeof the damage process during a wind event was summarized in Unanwa et al. (2000). From it, each building

156

S. HERATH and Y. WANG
Table 3

Descriptions of urban building’s damage due to wind.

Damage type

Damage description

Direct damage

Light damage to roof tile
Shingle removed, leaving decking exposed
Roof partly uncovered, light damage to structure
Half loss of roof sheeting, some structural damage
Change in roof-surface elevation
Severe damage to roof, loss of roof sheeting
Loss of roof structure, some damage to wall
Severe damage to structure, some collapse
Original roof edge are not intact
Loss of all wall
Collapse of some building
Total collapse of all building, etc.
Rain falling into house from opened roof and window
Function of the utilities inside house is damaged, etc.

Indirect damage

component may suffer damage either through the direct
impact of the wind or as a result of damage of othercomponents (i.e., damage propagation). Each building
component (except the structural system and interior) in
the damage model is connected with three lines. The
first line indicates its contribution to the propagational
damage of other components, while the second and thirdlines show the component’s direct (basic) damage andpropagational damage, respectively (Unanwa et al.,2000).
5.2 Relationships between wind speed and losses ofurban buildings
The loss due to wind on buildings is assessed
through two stages. In the first stage, the damage ratio isestablished as a function location which describes the
exposure and the wind speed. The former is estimatedfrom past data and its spatial variation should be known
for a given census track or a postal code zone. The relation between damage ratio for a given location and thewind speed, normally the gust speed, is used to obtain
the damage ratio. The second stage is the establishmentof loss rate which defined as the ratio of the insured loss
to a replacement value in the building. Again the loss
rate is expressed as a function of surface wind speeds,
termed loss rate curve, and is established from past
insured loss data. However, the uncertainty in determining the loss rate and wind speed is very large. The factors responsible for this uncertainty are the differencesin the building strengths, exterior material used, qualityof the building construction, etc (Watabe et al., 2005).
In the Hazus-MH model (Vickery et. al., 2006) a physical damage modeling approach is used where a given
storm is modeled and used to assess the damage on exterior component using a load resistance methodology.
Table 4 shows an example of the damage state
definitions used in Hazus-MH model for single-family

residential buildings. Such definitions are used for all
types of buildings defined in the model (Vickery et. al.,2006; Ishihara et al., 1995). As shown in Table 4 thedamage is categorized by five stages, varying from 0 (nodamage) to stage 4 which signify total collapse. The
mean number of particular building type expected toexperience a given damage state for a study region are
extracted from the model database, which are definedfor each census track. An example probability stage
damage ratio vs. peak gust wind is shown in Fig. 6.
Figures 7 and 8 show a number of damage ratio functions used by different organizations based on field data.
This does not distinguish among different damage levels.
Once the damage probability is known, the economic
loss associated with the damage to the building is estimated using the economic loss rate functions. Loss rate
defines the total loss ratio with respect to the building
cost. Some of the loss ratio functions used in industry
is shown in Fig. 7 (Haneka and Ruck, 2008). Thisapproach is compatible with the flood damage estimation method, where the percentage loss compared to thecost of the building is estimated by the flood loss
estimation models.
The wind speed of Ichinomiya during an example
flooding averaged over 10 min is shown in Fig. 9.
Ideally, the gust factor can be used to calculate the gustwind speed. However, the gust factor is influenced bymany factors, e.g., height, ground roughness condition,wind speed, etc., which makes the calculation of gustfactor complicated. In addition, the reliability of thecalculated gust factor is based on the quantity and quality of wind observation samples. In this study, due tolack of available data for the event simulated, we assumed the relationship between the maximum gust windspeed and the maximum average wind speed (10 minute
averaged) is linear. The peak gust wind speed in the
study area had been estimated as 50m/s based on aIncorporating Wind Damage in Potential Flood Loss Estimation

numerical model simulation (Watabe et. al., 2005).
AMeDAS (Automated Meteorological Data Acquisition
System) data were used to set the maximum averagewind speed for the area for the simulation period (1thOctober, 2002). The coefficient between the maximum
gust wind speed and the maximum average wind speed
can be thus estimated as 50m/s÷16m/s=3.1m/s. Then the
maximum gust wind speed in the flooding event is estimated as 11m/s*3.1=34.1m/s, where 11m/s is the average wind speed from AMeDAS records for the event.
This value may be considered to be on the higher side,
Table 4
Damagestate

157

but we used this value in the present study due to two
reasons. Firstly the factor we used as gust factor would
be more of a transfer coefficient from AMEDAS data tosimulated values in the absence of observed data.
Secondly, according to Okada et. al. (2002) new windload provisions in revised building code in Japan, gustfactors range from 2 (zone I) to 3.1 (zone IV). As per therecommendations of Architectural Institute of Japan(AIJ), zone I corresponds to open space where as zone
IV corresponds to city (rough) space in close proximity
to sea. The study site, Ichinomiya City, is located by theDamage States for Residential Construction Classes (Vickery et. al., 2006).
Qualitative damage description

Roof
cover
failureWindowdoor
failures

Roof
deck

Missileimpacts
on walls

Roofstructure
failureWallstructure
failure

≤2%

No

No

No

No

No

<5 impacts

No

No

0

No damage or very minor damage
Little or no visible damage from theoutside. No broken windows, or failed
roof deck. Minimal loss of roof over,with no or very limited water
penetration.

1

Minor damage
Maximum of one broken window, door,
or garage door. Moderate roof cover loss
>2% andthat can be covered to prevent additional≤15%
water entering the building. Marks ordents on walls requiring painting or
patching for repair.

One window,
door, orgarage door
failureNo

2Moderate damage
Major roof cover damage, moderate
window breakage. Minor roof sheathing
failure. Some resulting damage tointerior of building from water.

> one and ≤thelarger of 20%
and 3

1to3
panelsTypically
5to10
impacts

No

No

3

Severe damage
Major window damage or roof sheathing>50%
loss. Major roof cover loss. Extensive
damage to interior from water.

> the larger of20%
and 3 and≤50%

>3 and≤25%

Typically
10 to 20
impacts

No

No

4

Destruction
Complete roof failure and/or failure ofwall frame. Loss of more than 50% of
roof sheathing.

>50%

>25%

Typically>20
impacts

Yes

Yes>15% and≤50%

Typically>50%

Fig. 6 Example building damage state versus peak gust wind speed function (from Vickery et. al., 2006).

158

S. HERATH and Y. WANG

sea and is close to zone IV in this categorization.
Although the winds are not extensive to cause wide
spread damage, based on the Fig. 7, a loss rate of 0.5%
is assumed. Here, the definition of the loss rate is theratio of the insured loss to the replacement value in thebuilding (Watabe et al., 2005). The number of houses
flooded in the 1996 floods was 237 with average value
per structure at 170 million yen. If we assume all
flooded houses were subjected to roof damage, the total
loss amounts to 202 million yen. Compared with surveyed values in Fig. 4, this is 5% of structural building
damage, or 4% of total damage that include both
structural and content damage. Compared with simulated structural and total damage, the roof damage
amounts to 4% and 3% respectively.

Fig. 7 Wind damage used by different organizations(from Heneka and Ruck, 2008).

The total number of detached houses in Ichinomiya
basin was estimated as 30265. The number of flooded
houses, 237, constitutes 1% of the total houses. While
all flooded houses may not contain wind induced damage the houses that are not subjected to floods could be
affected by wind damage. Using the average of claim
ratios (as equivalent to damage ratio) from Fig. 7, wewould expect 1.5% of detached houses or 454 to bedamaged. With the same loss ratio as above, this would
amount to a loss of 387 million yen or about 8% of theestimated flood only losses to residential buildings (6%of simulated flood damage).

6. Conclusions
This paper explored combining existing wind loss
estimation methods with the flood loss estimation approach for urban losses associated with both floods andwinds. As flood losses are generally estimated from loss
functions associated with flood levels, it is possible thatassociated wind damage is not included in rapid loss
estimation, or when estimating potential climate related
losses. In the case study considered it is seen that wind
related damage could be around 4% of the flood damage
for flood-affected buildings, or about 8% of the flood
losses if basin wide wind damage is considered. However, further detailed case studies with wind field
information and ground survey are required to arrive at
range of additional losses that can be expected fromcombined extreme flood and intense wind events.
References

Fig. 8 Loss ratio functions of Huang (2001), Munich
Re (2001).

Fig. 9 Wind speed and rainfall averaged over 10 minutes
during the flooding event.

Ahern, M., S.R. Kovats, P. Wilkinson, R. Few and F. Matthies(2005) Global health impacts of floods: epidemiologic evidence,
Epidemiologic Reviews, 27: 36-46.
Berz, G., W. Kron, T. Loster, E. Rauch, J. Schimetschek, J.
Schmieder, A. Siebert, A. Smolka and A. Wirtz (2001) Worldmap of natural hazards – a global view of the distribution andintensity of significant exposures, Natural Hazards, 23 (2-3):
443-465.
Burby, R.J. (ed.) (1998) Cooperating with Nature: Confronting
Natural Hazards with Land-use Planning for Sustainable Communities. Joseph Henry Press, Washington, DC, 356 p.
Dutta, D., S. Herath and K. Musiake (2003) A mathematical model
for flood loss estimation. Journal of Hydrology, 277: 24-49.
French, J.G. and K.W. Holt (1989) Floods. In: Gregg M.B. (ed.)
The Public Health Consequences of Disasters. Atlanta,
Georgia: US Department of Health and Human Services, Public
Health Service, CDC, 69-78.
Herath, S., D. Dutta and K. Musiake (1999) Flood damage
estimation of an urban catchment using remote sensing and GIS.
Proc. the Eighth International Conference on Urban Storm
Drainage. August 30 - September 3, 1999, Sydney, Australia.
IB Joliffe and JE Ball (ed.), The Institution of Engineers
Australia, The International Association for Hydraulic Research,
and The International Association on Water Quality, 2177.
Heneka, P. and B. Ruck (2004). Development of a storm damage
risk map of Germany – A review of storm damage functions.
Proceedings of the International Conference for Disaster andSociety, Karlsruhe.
Heneka, P and B. Ruck (2008). A damage model for the assessmentof storm damage to buildings. Engineering Structures, 30:

Incorporating Wind Damage in Potential Flood Loss Estimation
3603-3609.
Huang, Z., D.V. Rosowsky and P.R. Sparks (2001) Hurricane
simulation techniques for the evaluation of wind-speeds andexpected insurance loss. Journal of Wind Eng. Ind. Aerodyn.
89: 605-671.
Ishihara, T., M. Matsui and K. Hibi (1995) Characteristics of thevertical wind profile in neutrally atomospheric boundary layers
Part 1: Strong winds during non – typhoon climates, Journal ofWind Engineering, 65: 1-15.
Jonkmana, S.N., M. Bočkarjova, M. Kok and P. Bernardini (2008)
Integrated hydrodynamic and economic modelling of flood
damage in the Netherlands. Ecological Economics, 66: 77-90.
Kates, R.W. (1965) Industrial flood losses: damage estimation in
the Lehigh Valley. Research Paper No. 98, Department ofGeography, University of Chicago, USA.
Kelman, I. and R. Spence (2004) An overview of flood actions onbuildings. Engineering Geology, 73 (3-4): 297-309.
Kreibich, H., A.H. Thieken, T. Petrow, M. Muller and B. Merz
(2005) Flood loss reduction of private households due to building precautionary measures – lessons learned from the Elbeflood in August 2002. Natural Hazards and Earth System Sciences, 5 (1): 117-126.
Krzysztofowicz, R. and D.R. Davis (1983) Category-unit loss
functions for flood forecast response system evaluation. Water
Resources Research, 19 (3): 1476-1480.
Kok, M., H.J. Huizinga, A.C.W.M. Vrouwenvelder and W.E.W.
van den Braak (2005) Standard method 2005 schade enslachtoff ers als gevolg van over stromingen, HKV report
PR999.10.
Munich Re (1997) Flooding and Insurance. Münchener Rückversicherungs-Gesellschaft, Munich.
Munich Re (2001) Wintersturme in Europa (II)– Schadensanalyse
1999 und Schadenspotentiale. Münchener RückversicherungsGesellschaft, Munich.
NAP, (1999) Impacts of Natural Disasters: A Framework for Loss
Estimation. National Academy Press, Washington DC, 68p.
OED (Oxford English dictionary) (2003) Oxford English Dictionary, on-line version. http://www.oed.com (accessed in September 2003).
OFDA/CRED International Disaster Database. http: //www.emdat.net, UCL – Brussels, Belgium.
Okada H., Y. Okuda and H. Kikitsu (2002). Wind load provisionsof the revised building code in Japan. Technical Note of
National Institute for Land and Infrastructure Management, 41:
67-71.
Parker, D.J., C.H. Green and P.M. Thompson (1987) Urban Flood
Protection Benefits – A Project Appraisal Guide. Gower Technical Press.
Penning-Rowsell, E.C. and J.B. Chatterton (1977) The Benefitsof Flood Alleviation – A Manual of Assessment Techniques.
Saxon House, ISBN 0566001908.
Pielke, J.R.A., M.W. Downton and M. Barnard (2002) Flood
Damage in the United States, 1926-2000: A Reanalysis of
National Weather Service Estimates. Boulder, CO, UCAR.
Smith, D.I. (1994) Flood damage estimation – a review of urban
stage-damage curves and loss functions. Water SA, 20:
231-238.
Stuyt, L.C.P.M., J.E.A. Reinders, E.E. van der Hoek, A.G.M.
Hermans, Keizer de M. Muinck, J.P. Kamperveen, M.C.
Hoogvliet, A.L. Gerritsen, M.W.A. Hounjet, M.P. Bontenbal
and Hoefsloot, Icke J. F.V.J. (2003) Environmental impact offlooding, Delft Cluster Report , DC1-233-8.
Thieken, A., M. Muller, H. Kreibich and B. Merz (2005) Flooddamage and influencing factors: new insights from the August
2002 flood in Germany. Water Resources Research, 41: 1-16.
UNDHA (United Nations Department of Humanitarian Affairs)(1992) Internationally Agreed Glossary of Basic Terms Related
to Disaster Management, UN DHA, Geneva, December 1992.
Unanwa, C.O., J.R. McDonald, K.C. Mehta and D.A. Smith

159(2000) The development of wind damage bands for buildings.
Journal of Wind Engineering and Industrial Aerodynamics, 84:
119-149.
Vickery, P.J., J. Lin, P.F. Skerlj, L.A. Twisdale and K. Huang(2006) HAZUS-MH Hurricane Model Methodology. I:
Hurricane Hazard, Terrain, and Wind Load Modeling. Natural
Hazards Review, 7(2): 82-93.
Watabe, H, T. Okazaki and T. Ishihara (2005) Issues in estimating
local damage by wind disaster – A case study in Chiba prefecture, Japan. The Fourth European & African Conference onWind Engineering. J. N´aprstek & C. Fischer (eds), ITAM AS
CR, Prague, 11-15 July, 2005, Paper #166.
White, G.F. (1964) Choice of adjustment to floods, Research
Paper No. 93, Department of Geography, University of
Chicago.

Srikantha HERATH
Dr. Herath is a Civil Engineer by training
specializing in Hydrology and Water Resources Engineering. His research interests are,• Hydrology and Water Resources: Monitoring and modeling of hydrologicalprocess and applications in urbanhydrology, flood forecasting, damage
estimation, sediment transport andwater cycle change assessment using physically based distributedmodels.
• Disaster Risk Reduction: Urban floods, Extreme events• Climate change and adaptation: global warming as well as global
dimming due to Atmospheric Brown Cloud, and impacts on water andagricultural sectors.
Currently he serves in the advisory committees of International Flood
Initiative, International programme on Landslides, as capacity development expert in Asia Pacific Network, as a member of the steering committee of Asian Pacific Water Forum on Water and Climate Change and anumber of other international and regional initiatives.
His previous positions include Associate Professor and Guest Foreign
Professor at University of Tokyo (1991-2002), senior research engineer inindustry, Tokyo (1988-1991), Research Associate in Asian Institute ofTechnology, Thailand, (1983-1984) and civil and irrigation engineer in Sri
Lanka (1980-1981). He has more than 150 publications in the fields ofhydrology and water resources. Dr. Herath holds Ph. D. in Civil Engineering from the University of Tokyo.

Yi WANGYi Wang is a JSPS-UNU Postdoctoral
Fellow in the Institute for Sustainability
and Peace, United Nations University. She
has strong research background andinterests on water resource management
and water related hazard analysis using
remote sensing technique, geographicalinformation system, and ecological models. She earned her master and doctoral
degree in 2004 and 2007, respectively, from Life and Environment Conservation Science Course at Ehime University in Japan. She has publishedmore than 20 scientific papers in international conferences, journals andbooks. With strong qualifications and experience, she has been awarded a
research fellowship from Japan Society for the Promotion of Sciences andnow is conducting a research about the impact of climate change andhuman activities on water related hazards in the international river basins.
The focus of her current research is modeling of hydrological process andapplications in forest catchment hydrology, flooding forecast, land slide
estimation, and water resource assessment using both lumped and distributed hydrological models aided by Remote Sensing and GIS technique.

(Received 1 December 2009, Accepted 25 December 2009)